Import Python Functions In Databricks: A Quick Guide

Oct 23, 2025 by Jhon Lennon 53 views

Importing Python Functions in Databricks: A Comprehensive Guide

Hey guys! Ever found yourself needing to reuse a function you've already written in another Python file within your Databricks notebook? It's a common scenario, and luckily, Databricks provides several ways to make this happen. This guide will walk you through the most effective methods, ensuring your code remains modular and easy to manage. Let's dive in!

Understanding the Basics of Importing in Python

Before we jump into the specifics of Databricks, let's quickly recap how importing works in Python. When you want to use code from another file, you use the import statement. There are a few ways to use it:

import module_name: This imports the entire module. You then access its functions using module_name.function_name(). It's like saying, "Hey Python, bring in the whole toolbox."
from module_name import function_name: This imports only the specified function. You can then use the function directly as function_name(). It's like saying, "Python, just give me this specific wrench from the toolbox."
from module_name import *: This imports all functions from the module. You can then use them directly. However, this is generally discouraged as it can lead to namespace collisions and make your code harder to understand. Think of it as emptying the entire toolbox onto your workspace – things can get messy!
import module_name as alias: This imports the entire module but gives it a shorter name. You then access its functions using alias.function_name(). It's like saying, "Python, bring in the toolbox, but let's call it 'tools' for short."

These basic import statements are the foundation for importing functions in any Python environment, including Databricks.

Method 1: Using `%run` to Execute Another Notebook

The %run magic command in Databricks is a simple way to execute another notebook within your current notebook. This is especially handy if your function is defined in a separate notebook. The %run command not only executes the target notebook but also makes its functions and variables available in the current notebook's scope.

Here’s how you can use it:

Create the Source Notebook: Let's say you have a notebook named my_functions_notebook that contains the following Python function:
```
# my_functions_notebook
def greet(name):
    return f"Hello, {name}!"
```

Import the Function using %run: In your main notebook, use the %run command to execute my_functions_notebook:

# Main notebook
%run ./my_functions_notebook

# Now you can use the greet function
message = greet("Databricks User")
print(message)

Important Considerations:

Pathing: Ensure the path to the notebook is correct. In the example above, ./ refers to the current directory. You might need to adjust the path based on your notebook's location within the Databricks workspace. Understanding the path is key to making this work smoothly.
Scope: Keep in mind that %run executes the target notebook, so any code in that notebook will be executed. This might include variable assignments or other operations that could affect your current notebook's state. Be mindful of potential side effects.
Simplicity: This method is excellent for quick and straightforward imports, especially when dealing with entire notebooks. It's super simple to get started with!

Method 2: Creating and Installing a Custom Python Package

For more complex projects or when you need to reuse functions across multiple notebooks and even different Databricks clusters, creating a custom Python package is the way to go. This involves structuring your code into a package, building it, and then installing it on your Databricks cluster.

Here’s a step-by-step guide:

Organize Your Code: Create a directory structure for your package. A basic structure looks like this:
```
my_package/
├── my_module.py
├── __init__.py
└── setup.py
```
- my_module.py: This file contains your functions.
```
# my_module.py
def add(x, y):
    return x + y
```
- __init__.py: This file can be empty or can contain initialization code for your package. At a minimum, it needs to exist to tell Python that the directory is a package.
```
# __init__.py
# You can leave this empty or add initialization code here
```
- setup.py: This file contains the metadata about your package and is used to build and install it.
```
# setup.py
from setuptools import setup, find_packages

setup(
    name='my_package',
    version='0.1.0',
    packages=find_packages(),
    install_requires=[],
)
```
Build the Package: Use the setup.py file to build a wheel file (a packaged format for Python distributions). Open your terminal, navigate to the my_package directory, and run:
```
python setup.py bdist_wheel
```
This will create a dist directory containing the .whl file (e.g., dist/my_package-0.1.0-py3-none-any.whl). You should see a new folder called dist..
Install the Package on Databricks:
- Upload the Wheel File: Upload the .whl file to DBFS (Databricks File System). You can do this through the Databricks UI or using the Databricks CLI.
- Install the Package: In a Databricks notebook, use the %pip command to install the package from DBFS:
```
%pip install /dbfs/path/to/your/package/dist/my_package-0.1.0-py3-none-any.whl
```
Import and Use the Function: Now you can import and use the functions from your package in your notebook:
```
from my_package.my_module import add

result = add(5, 3)
print(result)  # Output: 8
```

Key Advantages of Using Packages:

Reusability: Easily reuse your code across multiple notebooks and projects. This helps a ton when you have common functions.
Organization: Packages help you organize your code into logical modules, making it easier to maintain and understand. Organization is key to scalable projects.
Dependency Management: You can specify dependencies in the setup.py file, ensuring that all required libraries are installed along with your package. Managing dependencies is crucial for complex projects.

Method 3: Using `sys.path.append` (Less Recommended)

While not the most elegant solution, you can also modify the sys.path to include the directory containing your Python file. This allows you to import the file as a module.

Here’s how:

Locate the Directory: Identify the directory containing the Python file you want to import.

Modify sys.path: Use sys.path.append to add the directory to the Python path.

import sys
sys.path.append("/dbfs/path/to/your/directory")

import my_module  # Assuming your file is named my_module.py

# Now you can use functions from my_module
result = my_module.my_function(arguments)
print(result)

Why This Method Is Less Recommended:

Fragility: This approach is fragile because it relies on a specific file path. If the file moves, your code will break. Avoid this to keep your project healthy.
Scope: The changes to sys.path are only valid for the current Databricks session. If you restart, the paths will be lost.
Best Practices: It's generally better to use packages or %run for more robust and maintainable solutions. Stick to the recommended options.

Choosing the Right Method

So, which method should you choose? Here’s a quick guide:

%run: Use this for simple imports when you want to execute an entire notebook and make its functions available in the current notebook. Perfect for simple tasks..
Custom Python Package: This is the best option for larger projects where you need to reuse code across multiple notebooks and clusters. This is the pro way to do it..
sys.path.append: Avoid this method unless you have a very specific reason to use it, as it is less robust and maintainable. Use this at your own risk.

Conclusion

Importing functions from another Python file in Databricks is crucial for writing modular, reusable, and maintainable code. Whether you choose to use %run for simplicity or create a custom Python package for more complex projects, understanding these methods will significantly improve your Databricks development workflow. So go ahead, try them out, and make your code more organized and efficient!